QUESTION 1

Occupations

Step 1. Import the necessary libraries

Step 2. Import the dataset from this address

Step 3. Assign it to a variable called users

Step 4. Discover what is the mean age per occupation

Step 5. Discover the Male ratio per occupation and sort it from the most to the least

Step 6. For each occupation, calculate the minimum and maximum ages

Step 7. For each combination of occupation and sex, calculate the mean age

Step 8. For each occupation present the percentage of women and men

#step 1 Import the necessary libraries
import pandas as pd

#step 2 Import the dataset from this address
url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user'

#Step 3 Assign it to a variable called users
users = pd.read_csv(url,sep='|',index_col='user_id')
users

	age	gender	occupation	zip_code
user_id
1	24	M	technician	85711
2	53	F	other	94043
3	23	M	writer	32067
4	24	M	technician	43537
5	33	F	other	15213
...	...	...	...	...
939	26	F	student	33319
940	32	M	administrator	02215
941	20	M	student	97229
942	48	F	librarian	78209
943	22	M	student	77841

943 rows × 4 columns

#Step 4 Discover what is the mean age per occupation
users.groupby('occupation')['age'].mean()

occupation
administrator    38.746835
artist           31.392857
doctor           43.571429
educator         42.010526
engineer         36.388060
entertainment    29.222222
executive        38.718750
healthcare       41.562500
homemaker        32.571429
lawyer           36.750000
librarian        40.000000
marketing        37.615385
none             26.555556
other            34.523810
programmer       33.121212
retired          63.071429
salesman         35.666667
scientist        35.548387
student          22.081633
technician       33.148148
writer           36.311111
Name: age, dtype: float64

#Step 5 Discover the Male ratio per occupation and sort it from the most to the least
def genders(g):
    if g =='M':
        return 1
    else:
        return 0
users['gender_n'] = users['gender'].apply(genders)
x = users.groupby('occupation')['gender_n'].sum() / users.occupation.value_counts() * 100
x.sort_values(ascending=False)

doctor           100.000000
engineer          97.014925
technician        96.296296
retired           92.857143
programmer        90.909091
executive         90.625000
scientist         90.322581
entertainment     88.888889
lawyer            83.333333
salesman          75.000000
educator          72.631579
student           69.387755
other             65.714286
marketing         61.538462
writer            57.777778
none              55.555556
administrator     54.430380
artist            53.571429
librarian         43.137255
healthcare        31.250000
homemaker         14.285714
dtype: float64

#Step 6 For each occupation, calculate the minimum and maximum ages
users.groupby('occupation')['age'].agg(['min','max'])

	min	max
occupation
administrator	21	70
artist	19	48
doctor	28	64
educator	23	63
engineer	22	70
entertainment	15	50
executive	22	69
healthcare	22	62
homemaker	20	50
lawyer	21	53
librarian	23	69
marketing	24	55
none	11	55
other	13	64
programmer	20	63
retired	51	73
salesman	18	66
scientist	23	55
student	7	42
technician	21	55
writer	18	60

#Step 7 For each combination of occupation and sex, calculate the mean age
users.groupby(['occupation','gender'])['age'].mean()

occupation     gender
administrator  F         40.638889
               M         37.162791
artist         F         30.307692
               M         32.333333
doctor         M         43.571429
educator       F         39.115385
               M         43.101449
engineer       F         29.500000
               M         36.600000
entertainment  F         31.000000
               M         29.000000
executive      F         44.000000
               M         38.172414
healthcare     F         39.818182
               M         45.400000
homemaker      F         34.166667
               M         23.000000
lawyer         F         39.500000
               M         36.200000
librarian      F         40.000000
               M         40.000000
marketing      F         37.200000
               M         37.875000
none           F         36.500000
               M         18.600000
other          F         35.472222
               M         34.028986
programmer     F         32.166667
               M         33.216667
retired        F         70.000000
               M         62.538462
salesman       F         27.000000
               M         38.555556
scientist      F         28.333333
               M         36.321429
student        F         20.750000
               M         22.669118
technician     F         38.000000
               M         32.961538
writer         F         37.631579
               M         35.346154
Name: age, dtype: float64

#Step 8 For each occupation present the percentage of women and men
a = users.groupby(['occupation','gender']).agg({'gender':'count'})
b = users.groupby('occupation').agg('count')
c = a.div(b,level='occupation') * 100
c.loc[:,'gender']

occupation     gender
administrator  F          45.569620
               M          54.430380
artist         F          46.428571
               M          53.571429
doctor         M         100.000000
educator       F          27.368421
               M          72.631579
engineer       F           2.985075
               M          97.014925
entertainment  F          11.111111
               M          88.888889
executive      F           9.375000
               M          90.625000
healthcare     F          68.750000
               M          31.250000
homemaker      F          85.714286
               M          14.285714
lawyer         F          16.666667
               M          83.333333
librarian      F          56.862745
               M          43.137255
marketing      F          38.461538
               M          61.538462
none           F          44.444444
               M          55.555556
other          F          34.285714
               M          65.714286
programmer     F           9.090909
               M          90.909091
retired        F           7.142857
               M          92.857143
salesman       F          25.000000
               M          75.000000
scientist      F           9.677419
               M          90.322581
student        F          30.612245
               M          69.387755
technician     F           3.703704
               M          96.296296
writer         F          42.222222
               M          57.777778
Name: gender, dtype: float64

QUESTION 2

Euro Teams

Step 1. Import the necessary libraries

Step 2. Import the dataset from this address

Step 3. Assign it to a variable called euro12

Step 4. Select only the Goal column

Step 5. How many team participated in the Euro2012?

Step 6. What is the number of columns in the dataset?

Step 7. View only the columns Team, Yellow Cards and Red Cards and assign them to a dataframe called discipline

Step 8. Sort the teams by Red Cards, then to Yellow Cards

Step 9. Calculate the mean Yellow Cards given per Team

Step 10. Filter teams that scored more than 6 goals

Step 11. Select the teams that start with G

Step 12. Select the first 7 columns

Step 13. Select all columns except the last 3

Step 14. Present only the Shooting Accuracy from England, Italy and Russia

# Step 1 Import the necessary libraries
import pandas as pd

# Step 2 Import the dataset from this address
# Step 3 Assign it to a variable called euro12
euro12 = pd.read_csv('https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/02_Filtering_%26_Sorting/Euro12/Euro_2012_stats_TEAM.csv')
euro12

	Team	Goals	Shots on target	Shots off target	Shooting Accuracy	% Goals-to-shots	Total shots (inc. Blocked)	Hit Woodwork	Penalty goals	Penalties not scored	...	Saves made	Saves-to-shots ratio	Fouls Won	Fouls Conceded	Offsides	Yellow Cards	Red Cards	Subs on	Subs off	Players Used
0	Croatia	4	13	12	51.9%	16.0%	32	0	0	0	...	13	81.3%	41	62	2	9	0	9	9	16
1	Czech Republic	4	13	18	41.9%	12.9%	39	0	0	0	...	9	60.1%	53	73	8	7	0	11	11	19
2	Denmark	4	10	10	50.0%	20.0%	27	1	0	0	...	10	66.7%	25	38	8	4	0	7	7	15
3	England	5	11	18	50.0%	17.2%	40	0	0	0	...	22	88.1%	43	45	6	5	0	11	11	16
4	France	3	22	24	37.9%	6.5%	65	1	0	0	...	6	54.6%	36	51	5	6	0	11	11	19
5	Germany	10	32	32	47.8%	15.6%	80	2	1	0	...	10	62.6%	63	49	12	4	0	15	15	17
6	Greece	5	8	18	30.7%	19.2%	32	1	1	1	...	13	65.1%	67	48	12	9	1	12	12	20
7	Italy	6	34	45	43.0%	7.5%	110	2	0	0	...	20	74.1%	101	89	16	16	0	18	18	19
8	Netherlands	2	12	36	25.0%	4.1%	60	2	0	0	...	12	70.6%	35	30	3	5	0	7	7	15
9	Poland	2	15	23	39.4%	5.2%	48	0	0	0	...	6	66.7%	48	56	3	7	1	7	7	17
10	Portugal	6	22	42	34.3%	9.3%	82	6	0	0	...	10	71.5%	73	90	10	12	0	14	14	16
11	Republic of Ireland	1	7	12	36.8%	5.2%	28	0	0	0	...	17	65.4%	43	51	11	6	1	10	10	17
12	Russia	5	9	31	22.5%	12.5%	59	2	0	0	...	10	77.0%	34	43	4	6	0	7	7	16
13	Spain	12	42	33	55.9%	16.0%	100	0	1	0	...	15	93.8%	102	83	19	11	0	17	17	18
14	Sweden	5	17	19	47.2%	13.8%	39	3	0	0	...	8	61.6%	35	51	7	7	0	9	9	18
15	Ukraine	2	7	26	21.2%	6.0%	38	0	0	0	...	13	76.5%	48	31	4	5	0	9	9	18

16 rows × 35 columns

# Step 4 Select only the Goal column
euro12['Goals']

0      4
1      4
2      4
3      5
4      3
5     10
6      5
7      6
8      2
9      2
10     6
11     1
12     5
13    12
14     5
15     2
Name: Goals, dtype: int64

# Step 5 How many team participated in the Euro2012?
len(euro12['Team'].unique())

16

# Step 6 What is the number of columns in the dataset?
len(euro12.columns)

35

# Step 7 View only the columns Team, Yellow Cards and Red Cards and assign them to a dataframe called discipline
discipline=euro12[['Team','Yellow Cards','Red Cards']]
discipline

	Team	Yellow Cards	Red Cards
0	Croatia	9	0
1	Czech Republic	7	0
2	Denmark	4	0
3	England	5	0
4	France	6	0
5	Germany	4	0
6	Greece	9	1
7	Italy	16	0
8	Netherlands	5	0
9	Poland	7	1
10	Portugal	12	0
11	Republic of Ireland	6	1
12	Russia	6	0
13	Spain	11	0
14	Sweden	7	0
15	Ukraine	5	0

# Step 8 Sort the teams by Red Cards, then to Yellow Cards
discipline.sort_values(['Yellow Cards','Red Cards'])
df = discipline[['Red Cards','Yellow Cards']]
df

	Red Cards	Yellow Cards
0	0	9
1	0	7
2	0	4
3	0	5
4	0	6
5	0	4
6	1	9
7	0	16
8	0	5
9	1	7
10	0	12
11	1	6
12	0	6
13	0	11
14	0	7
15	0	5

# Step 9 Calculate the mean Yellow Cards given per Team
discipline.groupby('Team')['Yellow Cards'].mean()

Team
Croatia                 9
Czech Republic          7
Denmark                 4
England                 5
France                  6
Germany                 4
Greece                  9
Italy                  16
Netherlands             5
Poland                  7
Portugal               12
Republic of Ireland     6
Russia                  6
Spain                  11
Sweden                  7
Ukraine                 5
Name: Yellow Cards, dtype: int64

#Step 10 Filter teams that scored more than 6 goals
e = euro12[euro12.Goals > 6]
e[['Team','Goals']]

	Team	Goals
5	Germany	10
13	Spain	12

# Step 11 Select the teams that start with G
e = euro12[euro12.Team.str[0] == 'G']
e[['Team']]

	Team
5	Germany
6	Greece

# Step 12 Select the first 7 columns
euro12.iloc[: , 0:7]

	Team	Goals	Shots on target	Shots off target	Shooting Accuracy	% Goals-to-shots	Total shots (inc. Blocked)
0	Croatia	4	13	12	51.9%	16.0%	32
1	Czech Republic	4	13	18	41.9%	12.9%	39
2	Denmark	4	10	10	50.0%	20.0%	27
3	England	5	11	18	50.0%	17.2%	40
4	France	3	22	24	37.9%	6.5%	65
5	Germany	10	32	32	47.8%	15.6%	80
6	Greece	5	8	18	30.7%	19.2%	32
7	Italy	6	34	45	43.0%	7.5%	110
8	Netherlands	2	12	36	25.0%	4.1%	60
9	Poland	2	15	23	39.4%	5.2%	48
10	Portugal	6	22	42	34.3%	9.3%	82
11	Republic of Ireland	1	7	12	36.8%	5.2%	28
12	Russia	5	9	31	22.5%	12.5%	59
13	Spain	12	42	33	55.9%	16.0%	100
14	Sweden	5	17	19	47.2%	13.8%	39
15	Ukraine	2	7	26	21.2%	6.0%	38

# Step 13 Select all columns except the last 3
euro12.iloc[: , :-3]

	Team	Goals	Shots on target	Shots off target	Shooting Accuracy	% Goals-to-shots	Total shots (inc. Blocked)	Hit Woodwork	Penalty goals	Penalties not scored	...	Clean Sheets	Blocks	Goals conceded	Saves made	Saves-to-shots ratio	Fouls Won	Fouls Conceded	Offsides	Yellow Cards	Red Cards
0	Croatia	4	13	12	51.9%	16.0%	32	0	0	0	...	0	10	3	13	81.3%	41	62	2	9	0
1	Czech Republic	4	13	18	41.9%	12.9%	39	0	0	0	...	1	10	6	9	60.1%	53	73	8	7	0
2	Denmark	4	10	10	50.0%	20.0%	27	1	0	0	...	1	10	5	10	66.7%	25	38	8	4	0
3	England	5	11	18	50.0%	17.2%	40	0	0	0	...	2	29	3	22	88.1%	43	45	6	5	0
4	France	3	22	24	37.9%	6.5%	65	1	0	0	...	1	7	5	6	54.6%	36	51	5	6	0
5	Germany	10	32	32	47.8%	15.6%	80	2	1	0	...	1	11	6	10	62.6%	63	49	12	4	0
6	Greece	5	8	18	30.7%	19.2%	32	1	1	1	...	1	23	7	13	65.1%	67	48	12	9	1
7	Italy	6	34	45	43.0%	7.5%	110	2	0	0	...	2	18	7	20	74.1%	101	89	16	16	0
8	Netherlands	2	12	36	25.0%	4.1%	60	2	0	0	...	0	9	5	12	70.6%	35	30	3	5	0
9	Poland	2	15	23	39.4%	5.2%	48	0	0	0	...	0	8	3	6	66.7%	48	56	3	7	1
10	Portugal	6	22	42	34.3%	9.3%	82	6	0	0	...	2	11	4	10	71.5%	73	90	10	12	0
11	Republic of Ireland	1	7	12	36.8%	5.2%	28	0	0	0	...	0	23	9	17	65.4%	43	51	11	6	1
12	Russia	5	9	31	22.5%	12.5%	59	2	0	0	...	0	8	3	10	77.0%	34	43	4	6	0
13	Spain	12	42	33	55.9%	16.0%	100	0	1	0	...	5	8	1	15	93.8%	102	83	19	11	0
14	Sweden	5	17	19	47.2%	13.8%	39	3	0	0	...	1	12	5	8	61.6%	35	51	7	7	0
15	Ukraine	2	7	26	21.2%	6.0%	38	0	0	0	...	0	4	4	13	76.5%	48	31	4	5	0

16 rows × 32 columns

#Step 14. Present only the Shooting Accuracy from England, Italy and Russia
euro12.loc[[3,7,12] , ['Team','Shooting Accuracy']]

	Team	Shooting Accuracy
3	England	50.0%
7	Italy	43.0%
12	Russia	22.5%

QUESTION 3

Housing

Step 1. Import the necessary libraries

Step 2. Create 3 different Series, each of length 100, as follows:

• The first a random number from 1 to 4

• The second a random number from 1 to 3

• The third a random number from 10,000 to 30,000

Step 3. Create a DataFrame by joinning the Series by column

Step 4. Change the name of the columns to bedrs, bathrs, price_sqr_meter

Step 5. Create a one column DataFrame with the values of the 3 Series and assign it to 'bigcolumn'

Step 6. Ops it seems it is going only until index 99. Is it true?

Step 7. Reindex the DataFrame so it goes from 0 to 299

# Step 1 Import the necessary libraries
import pandas as pd
import numpy as np
import random

# Step 2. Create 3 differents Series, each of length 100, as follows:\n",
#The first a random number from 1 to 4
s1 = pd.Series(np.random.randint(1, high=5, size=100, dtype='l'))
s1

0     1
1     2
2     2
3     1
4     4
     ..
95    1
96    1
97    4
98    3
99    4
Length: 100, dtype: int32

#The second a random number from 1 to 3
s2 = pd.Series(np.random.randint(1, high=4, size=100, dtype='l'))
s2

0     3
1     3
2     3
3     2
4     1
     ..
95    2
96    1
97    3
98    2
99    2
Length: 100, dtype: int32

#The third a random number from 10,000 to 30,000
s3 = pd.Series(np.random.randint(10000, high=30001, size=100, dtype='l'))
s3

0     15665
1     10223
2     12305
3     16135
4     28406
      ...  
95    13590
96    19569
97    10837
98    22781
99    12075
Length: 100, dtype: int32

# Step 3 Create a DataFrame by joinning the Series by column
housings = pd.concat([s1, s2, s3], axis=1)
housings

	0	1	2
0	1	3	15665
1	2	3	10223
2	2	3	12305
3	1	2	16135
4	4	1	28406
...	...	...	...
95	1	2	13590
96	1	1	19569
97	4	3	10837
98	3	2	22781
99	4	2	12075

100 rows × 3 columns

# Step 4 Change the name of the columns to bedrs, bathrs, price_sqr_meter
housings.rename(columns = {0: 'bedrs', 1: 'bathrs', 2: 'price_sqr_meter'}, inplace=True)
housings

	bedrs	bathrs	price_sqr_meter
0	1	3	15665
1	2	3	10223
2	2	3	12305
3	1	2	16135
4	4	1	28406
...	...	...	...
95	1	2	13590
96	1	1	19569
97	4	3	10837
98	3	2	22781
99	4	2	12075

100 rows × 3 columns

# Step 5 Create a one column DataFrame with the values of the 3 Series and assign it to 'bigcolumn'
# join concat the values
bigcolumn = pd.DataFrame(s1.append(s2.append(s3)))
bigcolumn

	0
0	1
1	2
2	2
3	1
4	4
...	...
95	13590
96	19569
97	10837
98	22781
99	12075

300 rows × 1 columns

# Step 6 Ops it seems it is going only until index 99. Is it true?
bigcolumn.iloc[97:102,]

#YES, It is true

	0
97	4
98	3
99	4
0	3
1	3

# Step 7 Reindex the DataFrame so it goes from 0 to 299
bigcolumn.reset_index()

	index	0
0	0	1
1	1	2
2	2	2
3	3	1
4	4	4
...	...	...
295	95	13590
296	96	19569
297	97	10837
298	98	22781
299	99	12075

300 rows × 2 columns

QUESTION 4

Wind Statistics

The data have been modified to contain some missing values, identified by NaN.

Using pandas should make this exercise easier, in particular for the bonus question.

You should be able to perform all of these operations without using a for loop or other looping construct.

The data in 'wind.data' has the following format:

Yr Mo Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL CLO BEL MAL

61 1 1 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 12.58 18.50 15.04 61 1 2 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 9.67 17.54 13.83 61 1 3 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 7.67 12.75 12.71

The first three columns are year, month, and day. The remaining 12 columns are

average windspeeds in knots at 12 locations in Ireland on that day.

Step 1. Import the necessary libraries

Step 2. Import the dataset from this address

Step 3. Assign it to a variable called data and replace the first 3 columns by a proper datetime index.

Step 4. Year 2061? Do we really have data from this year? Create a function to fix it and apply it.

Step 5. Set the right dates as the index. Pay attention at the data type, it should be datetime64[ns].

Step 6. Compute how many values are missing for each location over the entire record.They should be ignored in all calculations below.

Step 7. Compute how many non-missing values there are in total.

Step 8. Calculate the mean windspeeds of the windspeeds over all the locations and all the times.

A single number for the entire dataset.

Step 9. Create a DataFrame called loc_stats and calculate the min, max and mean windspeeds and standard deviations of the windspeeds at each location over all the days

A different set of numbers for each location.

Step 10. Create a DataFrame called day_stats and calculate the min, max and mean windspeed and standard deviations of the windspeeds across all the locations at each day.

A different set of numbers for each day.

Step 11. Find the average windspeed in January for each location. Treat January 1961 and January 1962 both as January.

Step 12. Downsample the record to a yearly frequency for each location.

Step 13. Downsample the record to a monthly frequency for each location.

Step 14. Downsample the record to a weekly frequency for each location.

Step 15. Calculate the min, max and mean windspeeds and standard deviations of the windspeeds across all locations for each week (assume that the first week starts on January 2 1961) for the first 52 weeks.

# Step 1 Import the necessary libraries
import numpy as np
import pandas as pd
import datetime

# Step 2 Import the dataset from this address 
url="https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/06_Stats/Wind_Stats/wind.data"

# Step 3 Assign it to a variable called data and replace the first 3 columns by a proper datetime index
data=pd.read_csv(url,sep="\s+",parse_dates=[[0,1,2]])
data

	Yr_Mo_Dy	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
0	2061-01-01	15.04	14.96	13.17	9.29	NaN	9.87	13.67	10.25	10.83	12.58	18.50	15.04
1	2061-01-02	14.71	NaN	10.83	6.50	12.62	7.67	11.50	10.04	9.79	9.67	17.54	13.83
2	2061-01-03	18.50	16.88	12.33	10.13	11.17	6.17	11.25	NaN	8.50	7.67	12.75	12.71
3	2061-01-04	10.58	6.63	11.75	4.58	4.54	2.88	8.63	1.79	5.83	5.88	5.46	10.88
4	2061-01-05	13.33	13.25	11.42	6.17	10.71	8.21	11.92	6.54	10.92	10.34	12.92	11.83
...	...	...	...	...	...	...	...	...	...	...	...	...	...
6569	1978-12-27	17.58	16.96	17.62	8.08	13.21	11.67	14.46	15.59	14.04	14.00	17.21	40.08
6570	1978-12-28	13.21	5.46	13.46	5.00	8.12	9.42	14.33	16.25	15.25	18.05	21.79	41.46
6571	1978-12-29	14.00	10.29	14.42	8.71	9.71	10.54	19.17	12.46	14.50	16.42	18.88	29.58
6572	1978-12-30	18.50	14.04	21.29	9.13	12.75	9.71	18.08	12.87	12.46	12.12	14.67	28.79
6573	1978-12-31	20.33	17.41	27.29	9.59	12.08	10.13	19.25	11.63	11.58	11.38	12.08	22.08

6574 rows × 13 columns

# Step 4 Year 2061? Do we really have data from this year? Create a function to fix it and apply it
def fix_century(x):
    year=x.year-100 if x.year > 1979 else x.year
    return datetime.date(year,x.month,x.day)
data['Yr_Mo_Dy']=data['Yr_Mo_Dy'].apply(fix_century)
data.head()

	Yr_Mo_Dy	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
0	1961-01-01	15.04	14.96	13.17	9.29	NaN	9.87	13.67	10.25	10.83	12.58	18.50	15.04
1	1961-01-02	14.71	NaN	10.83	6.50	12.62	7.67	11.50	10.04	9.79	9.67	17.54	13.83
2	1961-01-03	18.50	16.88	12.33	10.13	11.17	6.17	11.25	NaN	8.50	7.67	12.75	12.71
3	1961-01-04	10.58	6.63	11.75	4.58	4.54	2.88	8.63	1.79	5.83	5.88	5.46	10.88
4	1961-01-05	13.33	13.25	11.42	6.17	10.71	8.21	11.92	6.54	10.92	10.34	12.92	11.83

# Step 5 Set the right dates as the index. Pay attention at the data type, it should be datetime64[ns]
data['Yr_Mo_Dy']=pd.to_datetime(data['Yr_Mo_Dy'])
data=data.set_index('Yr_Mo_Dy')
data.head()

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
Yr_Mo_Dy
1961-01-01	15.04	14.96	13.17	9.29	NaN	9.87	13.67	10.25	10.83	12.58	18.50	15.04
1961-01-02	14.71	NaN	10.83	6.50	12.62	7.67	11.50	10.04	9.79	9.67	17.54	13.83
1961-01-03	18.50	16.88	12.33	10.13	11.17	6.17	11.25	NaN	8.50	7.67	12.75	12.71
1961-01-04	10.58	6.63	11.75	4.58	4.54	2.88	8.63	1.79	5.83	5.88	5.46	10.88
1961-01-05	13.33	13.25	11.42	6.17	10.71	8.21	11.92	6.54	10.92	10.34	12.92	11.83

# Step 6 Compute how many values are missing for each location over the entire record
#They should be ignored in all calculations below.
data.isnull().sum()

RPT    6
VAL    3
ROS    2
KIL    5
SHA    2
BIR    0
DUB    3
CLA    2
MUL    3
CLO    1
BEL    0
MAL    4
dtype: int64

# Step 7 Compute how many non-missing values there are in total
data.notnull().sum()

RPT    6568
VAL    6571
ROS    6572
KIL    6569
SHA    6572
BIR    6574
DUB    6571
CLA    6572
MUL    6571
CLO    6573
BEL    6574
MAL    6570
dtype: int64

# Step 8 Calculate the mean windspeeds of the windspeeds over all the locations and all the times
data.mean().mean()

10.227982360836924

# Step 9 Create a DataFrame called loc_stats and calculate the min, max and mean windspeeds and standard deviations of the windspeeds at each location over all the days
loc_stats = pd.DataFrame({
    'min': data.min(),
    'max': data.max(),
    'mean': data.mean(),
    'std': data.std()
})

loc_stats

	min	max	mean	std
RPT	0.67	35.80	12.362987	5.618413
VAL	0.21	33.37	10.644314	5.267356
ROS	1.50	33.84	11.660526	5.008450
KIL	0.00	28.46	6.306468	3.605811
SHA	0.13	37.54	10.455834	4.936125
BIR	0.00	26.16	7.092254	3.968683
DUB	0.00	30.37	9.797343	4.977555
CLA	0.00	31.08	8.495053	4.499449
MUL	0.00	25.88	8.493590	4.166872
CLO	0.04	28.21	8.707332	4.503954
BEL	0.13	42.38	13.121007	5.835037
MAL	0.67	42.54	15.599079	6.699794

data.describe()

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
count	6568.000000	6571.000000	6572.000000	6569.000000	6572.000000	6574.000000	6571.000000	6572.000000	6571.000000	6573.000000	6574.000000	6570.000000
mean	12.362987	10.644314	11.660526	6.306468	10.455834	7.092254	9.797343	8.495053	8.493590	8.707332	13.121007	15.599079
std	5.618413	5.267356	5.008450	3.605811	4.936125	3.968683	4.977555	4.499449	4.166872	4.503954	5.835037	6.699794
min	0.670000	0.210000	1.500000	0.000000	0.130000	0.000000	0.000000	0.000000	0.000000	0.040000	0.130000	0.670000
25%	8.120000	6.670000	8.000000	3.580000	6.750000	4.000000	6.000000	5.090000	5.370000	5.330000	8.710000	10.710000
50%	11.710000	10.170000	10.920000	5.750000	9.960000	6.830000	9.210000	8.080000	8.170000	8.290000	12.500000	15.000000
75%	15.920000	14.040000	14.670000	8.420000	13.540000	9.670000	12.960000	11.420000	11.190000	11.630000	16.880000	19.830000
max	35.800000	33.370000	33.840000	28.460000	37.540000	26.160000	30.370000	31.080000	25.880000	28.210000	42.380000	42.540000

# Step 10 Create a DataFrame called day_stats and calculate the min, max and mean windspeed and standard deviations of the windspeeds across all the locations at each day.
day_stats = pd.DataFrame({
    'min': data.min(axis=1),
    'max': data.max(axis=1),
    'mean': data.mean(axis=1),
    'std': data.std(axis=1)
})

day_stats

	min	max	mean	std
Yr_Mo_Dy
1961-01-01	9.29	18.50	13.018182	2.808875
1961-01-02	6.50	17.54	11.336364	3.188994
1961-01-03	6.17	18.50	11.641818	3.681912
1961-01-04	1.79	11.75	6.619167	3.198126
1961-01-05	6.17	13.33	10.630000	2.445356
...	...	...	...	...
1978-12-27	8.08	40.08	16.708333	7.868076
1978-12-28	5.00	41.46	15.150000	9.687857
1978-12-29	8.71	29.58	14.890000	5.756836
1978-12-30	9.13	28.79	15.367500	5.540437
1978-12-31	9.59	27.29	15.402500	5.702483

6574 rows × 4 columns

#Step 11 Find the average windspeed in January for each location. Treat January 1961 and January 1962 both as January

data['date']=data.index
data['month']=data['date'].apply(lambda date:date.month)
data['year']=data['date'].apply(lambda date:date.year)
data['day']=data['date'].apply(lambda date:date.day)
jan_winds=data.query('month==1')
jan_winds.loc[:,'RPT':'MAL'].mean()

RPT    14.847325
VAL    12.914560
ROS    13.299624
KIL     7.199498
SHA    11.667734
BIR     8.054839
DUB    11.819355
CLA     9.512047
MUL     9.543208
CLO    10.053566
BEL    14.550520
MAL    18.028763
dtype: float64

#Step 12 Downsample the record to a yearly frequency for each location.
data.query('month == 1 and day == 1')

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL	date	month	year	day
Yr_Mo_Dy
1961-01-01	15.04	14.96	13.17	9.29	NaN	9.87	13.67	10.25	10.83	12.58	18.50	15.04	1961-01-01	1	1961	1
1962-01-01	9.29	3.42	11.54	3.50	2.21	1.96	10.41	2.79	3.54	5.17	4.38	7.92	1962-01-01	1	1962	1
1963-01-01	15.59	13.62	19.79	8.38	12.25	10.00	23.45	15.71	13.59	14.37	17.58	34.13	1963-01-01	1	1963	1
1964-01-01	25.80	22.13	18.21	13.25	21.29	14.79	14.12	19.58	13.25	16.75	28.96	21.00	1964-01-01	1	1964	1
1965-01-01	9.54	11.92	9.00	4.38	6.08	5.21	10.25	6.08	5.71	8.63	12.04	17.41	1965-01-01	1	1965	1
1966-01-01	22.04	21.50	17.08	12.75	22.17	15.59	21.79	18.12	16.66	17.83	28.33	23.79	1966-01-01	1	1966	1
1967-01-01	6.46	4.46	6.50	3.21	6.67	3.79	11.38	3.83	7.71	9.08	10.67	20.91	1967-01-01	1	1967	1
1968-01-01	30.04	17.88	16.25	16.25	21.79	12.54	18.16	16.62	18.75	17.62	22.25	27.29	1968-01-01	1	1968	1
1969-01-01	6.13	1.63	5.41	1.08	2.54	1.00	8.50	2.42	4.58	6.34	9.17	16.71	1969-01-01	1	1969	1
1970-01-01	9.59	2.96	11.79	3.42	6.13	4.08	9.00	4.46	7.29	3.50	7.33	13.00	1970-01-01	1	1970	1
1971-01-01	3.71	0.79	4.71	0.17	1.42	1.04	4.63	0.75	1.54	1.08	4.21	9.54	1971-01-01	1	1971	1
1972-01-01	9.29	3.63	14.54	4.25	6.75	4.42	13.00	5.33	10.04	8.54	8.71	19.17	1972-01-01	1	1972	1
1973-01-01	16.50	15.92	14.62	7.41	8.29	11.21	13.54	7.79	10.46	10.79	13.37	9.71	1973-01-01	1	1973	1
1974-01-01	23.21	16.54	16.08	9.75	15.83	11.46	9.54	13.54	13.83	16.66	17.21	25.29	1974-01-01	1	1974	1
1975-01-01	14.04	13.54	11.29	5.46	12.58	5.58	8.12	8.96	9.29	5.17	7.71	11.63	1975-01-01	1	1975	1
1976-01-01	18.34	17.67	14.83	8.00	16.62	10.13	13.17	9.04	13.13	5.75	11.38	14.96	1976-01-01	1	1976	1
1977-01-01	20.04	11.92	20.25	9.13	9.29	8.04	10.75	5.88	9.00	9.00	14.88	25.70	1977-01-01	1	1977	1
1978-01-01	8.33	7.12	7.71	3.54	8.50	7.50	14.71	10.00	11.83	10.00	15.09	20.46	1978-01-01	1	1978	1

# Step 13 Downsample the record to a monthly frequency for each location
data.query('day == 1')

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL	date	month	year	day
Yr_Mo_Dy
1961-01-01	15.04	14.96	13.17	9.29	NaN	9.87	13.67	10.25	10.83	12.58	18.50	15.04	1961-01-01	1	1961	1
1961-02-01	14.25	15.12	9.04	5.88	12.08	7.17	10.17	3.63	6.50	5.50	9.17	8.00	1961-02-01	2	1961	1
1961-03-01	12.67	13.13	11.79	6.42	9.79	8.54	10.25	13.29	NaN	12.21	20.62	NaN	1961-03-01	3	1961	1
1961-04-01	8.38	6.34	8.33	6.75	9.33	9.54	11.67	8.21	11.21	6.46	11.96	7.17	1961-04-01	4	1961	1
1961-05-01	15.87	13.88	15.37	9.79	13.46	10.17	9.96	14.04	9.75	9.92	18.63	11.12	1961-05-01	5	1961	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1978-08-01	19.33	15.09	20.17	8.83	12.62	10.41	9.33	12.33	9.50	9.92	15.75	18.00	1978-08-01	8	1978	1
1978-09-01	8.42	6.13	9.87	5.25	3.21	5.71	7.25	3.50	7.33	6.50	7.62	15.96	1978-09-01	9	1978	1
1978-10-01	9.50	6.83	10.50	3.88	6.13	4.58	4.21	6.50	6.38	6.54	10.63	14.09	1978-10-01	10	1978	1
1978-11-01	13.59	16.75	11.25	7.08	11.04	8.33	8.17	11.29	10.75	11.25	23.13	25.00	1978-11-01	11	1978	1
1978-12-01	21.29	16.29	24.04	12.79	18.21	19.29	21.54	17.21	16.71	17.83	17.75	25.70	1978-12-01	12	1978	1

216 rows × 16 columns

# Step 15 Calculate the min, max and mean windspeeds and standard deviations of the windspeeds across all locations for each week (assume that the first week starts on January 2 1961) for the first 52 weeks
df_1961 = data[data.index < pd.to_datetime('1962-01-01')]
df_1961.resample('W').mean()
df_1961.resample('W').min()
df_1961.resample('W').max()
df_1961.resample('W').std()

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL	month	year	day
Yr_Mo_Dy
1961-01-01	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1961-01-08	2.631321	3.949525	1.604761	1.810743	3.251660	2.059546	1.872222	3.098404	1.722255	1.704941	4.349139	1.773062	0.000000	0.0	2.160247
1961-01-15	3.555392	3.148945	5.034959	3.549559	3.471726	3.251039	4.709309	3.936894	3.500975	4.084293	5.552215	4.692355	0.000000	0.0	2.160247
1961-01-22	5.337402	3.837785	5.086229	6.245541	3.612875	3.453432	5.166300	3.164990	4.169112	4.783952	3.626584	4.237239	0.000000	0.0	2.160247
1961-01-29	4.619061	5.170224	4.665843	4.301325	4.858116	3.749415	4.508449	4.436222	4.902057	3.713368	5.210726	3.874721	0.000000	0.0	2.160247
1961-02-05	5.251408	5.187395	3.975166	2.709106	2.334619	2.397066	2.423454	4.081158	2.802490	2.839501	4.210858	4.336104	0.487950	0.0	13.483676
1961-02-12	3.587677	3.608373	3.290303	2.262056	5.571108	3.048976	2.974059	3.022753	2.914760	1.746749	4.063753	1.828705	0.000000	0.0	2.160247
1961-02-19	5.064609	3.575012	4.196621	4.311569	2.321716	3.024078	4.958631	2.283444	2.560591	2.531361	5.910938	4.685377	0.000000	0.0	2.160247
1961-02-26	7.020716	5.147348	5.578470	4.482075	6.480712	5.029874	6.037916	4.869668	4.705163	4.920064	5.091162	6.182283	0.000000	0.0	2.160247
1961-03-05	0.997721	2.851955	1.796871	1.652572	2.957129	2.022247	3.338177	2.877395	2.610124	1.593685	4.332331	3.021387	0.487950	0.0	12.027746
1961-03-12	3.732263	3.230167	3.592909	2.609928	3.110857	3.440419	5.537269	3.760557	3.657690	3.655113	4.358759	5.769890	0.000000	0.0	2.160247
1961-03-19	3.860036	2.352867	2.939244	2.416746	2.654289	2.045448	4.046593	3.373421	3.301880	3.099472	3.779727	4.331958	0.000000	0.0	2.160247
1961-03-26	3.613298	3.657265	4.041121	2.410127	2.927188	2.285729	2.892270	2.789682	2.469432	2.538224	4.318069	3.701846	0.000000	0.0	2.160247
1961-04-02	5.046922	4.687315	3.678996	3.625488	5.657679	4.054639	4.047068	4.689504	4.025317	3.191115	4.179854	3.924555	0.487950	0.0	13.483676
1961-04-09	4.604392	2.845399	2.902991	2.854052	2.743895	2.525046	2.109769	1.996609	1.935058	2.336182	3.147781	2.598271	0.000000	0.0	2.160247
1961-04-16	3.937727	2.607118	4.585752	2.664416	1.941836	2.388107	1.970667	2.379446	2.123279	2.161137	3.641464	2.747842	0.000000	0.0	2.160247
1961-04-23	5.676655	4.631736	5.456290	3.835403	4.122437	3.835998	3.254663	3.935731	3.656682	3.347972	4.735096	5.908542	0.000000	0.0	2.160247
1961-04-30	4.349662	2.871425	3.732776	1.897616	3.259907	2.528436	3.565777	3.426255	2.718926	2.840568	2.948237	5.108365	0.000000	0.0	2.160247
1961-05-07	5.025507	3.750835	4.301778	3.141495	5.781916	3.324038	4.419008	4.461950	3.558856	3.620819	8.003490	7.728504	0.000000	0.0	2.160247
1961-05-14	3.371022	3.782947	3.002225	3.509695	4.813159	4.660173	5.744586	4.690361	5.126826	5.460237	3.968272	7.858246	0.000000	0.0	2.160247
1961-05-21	3.631730	2.468906	3.767505	0.668791	2.541271	1.377071	1.650089	2.221636	1.613885	2.216889	1.975853	3.310819	0.000000	0.0	2.160247
1961-05-28	2.739433	3.378537	6.355768	1.378803	2.945134	1.873381	2.350104	3.207147	1.990891	2.575661	3.024524	3.811818	0.000000	0.0	2.160247
1961-06-04	3.099701	1.868125	3.474607	2.323826	2.305695	1.332009	2.300028	0.915184	1.954609	2.096989	2.611139	2.593586	0.534522	0.0	14.738999
1961-06-11	2.248597	1.524836	1.887475	1.603775	2.238228	2.204087	2.514689	2.857527	1.695090	2.158323	3.993062	4.925055	0.000000	0.0	2.160247
1961-06-18	3.009482	3.509444	3.429057	2.130352	4.175947	3.292713	3.417989	4.858023	2.900464	3.792400	6.477887	6.242673	0.000000	0.0	2.160247
1961-06-25	1.982035	2.212460	1.916454	1.332234	1.422401	1.448318	2.455385	2.109358	2.042257	2.286218	2.498386	3.063011	0.000000	0.0	2.160247
1961-07-02	2.557856	2.902411	1.161629	1.911018	2.172125	1.626363	1.927673	3.065467	1.745125	1.564144	6.303747	3.652313	0.487950	0.0	12.998168
1961-07-09	3.664855	2.686658	2.995919	2.898890	3.661383	2.459639	3.493425	3.160424	3.453210	3.657179	4.537988	3.665705	0.000000	0.0	2.160247
1961-07-16	5.168710	3.849630	2.166206	2.665625	4.596567	3.164715	4.489523	4.083629	3.978635	3.271899	4.971060	4.974273	0.000000	0.0	2.160247
1961-07-23	1.047978	1.400010	2.783208	1.158908	0.764956	0.617815	1.757905	1.479152	0.873207	1.439785	2.050218	2.133994	0.000000	0.0	2.160247
1961-07-30	4.157641	3.203206	3.276465	2.843207	4.514505	3.786633	5.110195	4.060478	4.504844	4.350268	5.580903	6.664574	0.000000	0.0	2.160247
1961-08-06	2.950887	3.985226	3.483894	1.647275	4.189524	2.642246	3.709319	3.921508	3.217501	2.903018	4.901377	4.448251	0.377964	0.0	10.533394
1961-08-13	4.422268	2.053326	2.174407	1.921080	3.071872	2.045195	3.073187	2.088368	2.718228	2.073777	2.931302	3.356585	0.000000	0.0	2.160247
1961-08-20	2.283635	2.523416	1.766039	1.938109	2.568539	2.227127	3.197566	2.972809	2.339544	2.730237	4.086725	3.934238	0.000000	0.0	2.160247
1961-08-27	3.395857	3.174702	3.071403	2.455711	4.380624	3.416186	4.170083	4.900671	4.120185	3.855302	6.711322	4.947608	0.000000	0.0	2.160247
1961-09-03	4.398615	7.474025	4.395383	3.790030	6.128522	4.455552	3.920110	5.012759	4.155931	3.993736	7.678051	6.308087	0.534522	0.0	14.738999
1961-09-10	5.207278	4.003996	3.922643	3.151502	3.178791	2.965546	4.258696	3.074692	3.767708	3.649278	4.220584	6.049619	0.000000	0.0	2.160247
1961-09-17	7.679190	5.360585	6.638947	5.560509	6.279010	4.706530	4.235755	4.565163	4.847073	5.128338	4.464252	6.332885	0.000000	0.0	2.160247
1961-09-24	1.267399	3.445262	2.322229	1.609614	3.125219	2.293031	1.444916	3.342247	1.523142	2.354092	5.235868	3.113507	0.000000	0.0	2.160247
1961-10-01	4.559572	2.812482	4.201062	2.050553	3.353967	1.901786	2.098203	3.425963	1.790306	3.908397	5.091268	4.696504	0.377964	0.0	10.160615
1961-10-08	5.596710	5.060803	4.394234	3.249050	3.830935	3.686641	3.420894	3.313201	3.717239	4.296870	4.800403	5.462002	0.000000	0.0	2.160247
1961-10-15	4.780675	2.707483	5.979099	2.367850	2.246657	1.969085	3.409743	2.506401	2.947025	3.569308	4.113200	4.098130	0.000000	0.0	2.160247
1961-10-22	7.888314	5.998199	5.463782	4.989763	6.095112	3.801637	5.334910	5.199929	3.785875	5.890511	5.645871	7.468377	0.000000	0.0	2.160247
1961-10-29	7.957637	6.879973	7.428776	5.576503	7.344744	5.101188	4.542684	5.506544	4.448500	6.277629	7.056150	8.340881	0.000000	0.0	2.160247
1961-11-05	3.369201	3.900278	2.461109	2.448363	3.664806	2.513051	2.727816	3.599111	3.003274	2.784450	4.038493	3.870800	0.487950	0.0	13.483676
1961-11-12	3.939811	2.141191	6.779554	2.930324	2.090641	1.414308	2.388943	2.181840	2.153102	2.843518	2.532196	3.690752	0.000000	0.0	2.160247
1961-11-19	2.784358	3.208548	9.415716	4.176374	2.092809	3.440953	7.027159	3.452202	3.488521	4.402588	3.643285	3.787654	0.000000	0.0	2.160247
1961-11-26	3.214368	3.277904	3.622254	1.824938	2.132751	1.822767	3.551685	2.276663	2.392934	2.747452	5.407223	6.475867	0.000000	0.0	2.160247
1961-12-03	5.704669	5.107089	4.163650	4.097123	5.536334	3.534980	4.186505	3.744471	3.658796	3.680477	5.552648	5.233192	0.534522	0.0	14.205968
1961-12-10	4.890152	4.115506	4.682044	3.782631	3.345941	3.481252	4.357862	4.503161	3.452864	4.156207	4.667933	7.345893	0.000000	0.0	2.160247
1961-12-17	4.095106	3.587886	3.843380	3.501489	3.943048	3.312448	6.474478	3.482027	4.333020	4.633398	6.531043	5.665006	0.000000	0.0	2.160247
1961-12-24	4.959717	2.220866	5.653229	5.341288	4.898600	4.019469	5.724775	4.134367	4.243508	4.637096	5.065308	5.048035	0.000000	0.0	2.160247
1961-12-31	5.787783	4.566479	9.739918	4.167851	3.018856	2.674370	5.142213	4.289240	3.325214	3.526625	3.262217	3.012729	0.000000	0.0	2.160247

QUESTION 5

Step 1. Import the necessary libraries

Step 2. Import the dataset from this address.

Step 3. Assign it to a variable called chipo.

Step 4. See the first 10 entries

Step 5. What is the number of observations in the dataset?

Step 6. What is the number of columns in the dataset?

Step 7. Print the name of all the columns.

Step 8. How is the dataset indexed?

Step 9. Which was the most-ordered item?

Step 10. For the most-ordered item, how many items were ordered?

Step 11. What was the most ordered item in the choice_description column?

Step 12. How many items were orderd in total?

Step 13.

• Turn the item price into a float

• Check the item price type

• Create a lambda function and change the type of item price

• Check the item price type

Step 14. How much was the revenue for the period in the dataset?

Step 15. How many orders were made in the period?

Step 16. What is the average revenue amount per order?

Step 17. How many different items are sold?

# Step 1 Import the necessary libraries
import pandas as pd

# Step 2 Import the dataset from this address.
url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'

# Step 3 Assign it to a variable called chipo
chipo = pd.read_csv(url, sep = '\t')
chipo

	order_id	quantity	item_name	choice_description	item_price
0	1	1	Chips and Fresh Tomato Salsa	NaN	$2.39
1	1	1	Izze	[Clementine]	$3.39
2	1	1	Nantucket Nectar	[Apple]	$3.39
3	1	1	Chips and Tomatillo-Green Chili Salsa	NaN	$2.39
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$16.98
...	...	...	...	...	...
4617	1833	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...	$11.75
4618	1833	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...	$11.75
4619	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$11.25
4620	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...	$8.75
4621	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$8.75

4622 rows × 5 columns

#Step 4 See the first 10 entries
chipo.head(10)

	order_id	quantity	item_name	choice_description	item_price
0	1	1	Chips and Fresh Tomato Salsa	NaN	$2.39
1	1	1	Izze	[Clementine]	$3.39
2	1	1	Nantucket Nectar	[Apple]	$3.39
3	1	1	Chips and Tomatillo-Green Chili Salsa	NaN	$2.39
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$16.98
5	3	1	Chicken Bowl	[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...	$10.98
6	3	1	Side of Chips	NaN	$1.69
7	4	1	Steak Burrito	[Tomatillo Red Chili Salsa, [Fajita Vegetables...	$11.75
8	4	1	Steak Soft Tacos	[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...	$9.25
9	5	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...	$9.25

# Step 5 What is the number of observations in the dataset?
chipo.shape[0]

4622

# Step 6 What is the number of columns in the dataset?
chipo.shape[1]

5

#step 5 & 6 Display number of rows & columns
chipo.shape

(4622, 5)

# Step 7 Print the name of all the columns
chipo.columns

Index(['order_id', 'quantity', 'item_name', 'choice_description',
       'item_price'],
      dtype='object')

# Step 8 How is the dataset indexed?
chipo.index

RangeIndex(start=0, stop=4622, step=1)

# Which was the most-ordered item?
chipo_grp = chipo.groupby('item_name')['quantity']
chip = chipo_grp.sum()
print('The most-ordered item is {} and has been order {} times.'.format(chip.idxmax(), chip.max()))

The most-ordered item is Chicken Bowl and has been order 761 times.

# What was the most ordered item in the choice_description column?
chipo_desc_grp = chipo.groupby('choice_description')['quantity']
chip = chipo_desc_grp.sum()
print('The most ordered item in the choice_description column is {}'.format(chip.idxmax()))
print('And it has been ordered {} times'.format(chip.max()))

The most ordered item in the choice_description column is [Diet Coke]
And it has been ordered 159 times

# Step 12 How many items were orderd in total?
chipo.quantity.sum()

4972

# Step 13
# Turn the item price into a float
def num_float(x):
    return float(x[1:-1])
chipo.item_price = chipo.item_price.apply(num_float)
chipo

	order_id	quantity	item_name	choice_description	item_price
0	1	1	Chips and Fresh Tomato Salsa	NaN	2.39
1	1	1	Izze	[Clementine]	3.39
2	1	1	Nantucket Nectar	[Apple]	3.39
3	1	1	Chips and Tomatillo-Green Chili Salsa	NaN	2.39
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	16.98
...	...	...	...	...	...
4617	1833	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...	11.75
4618	1833	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...	11.75
4619	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	11.25
4620	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...	8.75
4621	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	8.75

4622 rows × 5 columns

# Step 14 How much was the revenue for the period in the dataset?
chipo.item_price.sum()

34500.16

# Step 15 How many orders were made in the period?
chipo.order_id.value_counts().count()

1834

# Step 16 What is the average revenue amount per order?
order_grouped = chipo.groupby(by=['order_id']).sum()
order_grouped['quantity'].mean()

2.711014176663032

# Step 17 What are the three most expensvie items?
chipo['unit_price'] = (chipo['item_price'] / chipo['quantity'])
new = chipo[['item_name', 'choice_description', 'unit_price']]
new.sort_values(by='unit_price', ascending=False)

	item_name	choice_description	unit_price
2442	Barbacoa Salad Bowl	[Tomatillo Green Chili Salsa, [Rice, Fajita Ve...	11.89
2957	Steak Salad Bowl	[Fresh Tomato Salsa, [Black Beans, Cheese, Gua...	11.89
613	Steak Salad Bowl	[Tomatillo Red Chili Salsa, [Fajita Vegetables...	11.89
2401	Steak Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Guaca...	11.89
281	Steak Salad Bowl	[Tomatillo Red Chili Salsa, [Black Beans, Chee...	11.89
...	...	...	...
378	Canned Soda	[Mountain Dew]	1.09
1805	Canned Soda	[Sprite]	1.09
2520	Canned Soda	[Coca Cola]	1.09
2981	Bottled Water	NaN	1.09
646	Canned Soda	[Sprite]	1.09

4622 rows × 3 columns

QUESTION 6

Create a line plot showing the number of marriages and divorces per capita in the U.S. between 1867 and 2014. Label both lines and show the legend.Don't forget to label your axes!

import pandas as pd
df=pd.read_csv('us-marriages-divorces-1867-2014.csv')
import matplotlib.pyplot as plt
df.plot('Year',y=['Marriages','Divorces'],color=['purple','red'],figsize=(15,10))
plt.xlabel('Year',fontsize=14,color='blue')
plt.show()

QUESTION 7

Create a vertical bar chart comparing the number of marriages and divorces per capita in the U.S. between 1900, 1950, and 2000. Don't forget to label your axes!

df1 = df[df['Year'] >= 1900 ]
df2 = df1[df['Year'] <=2000 ]
df2

<ipython-input-59-f3c155301355>:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
  df2 = df1[df['Year'] <=2000 ]

	Year	Marriages	Divorces	Population	Marriages_per_1000	Divorces_per_1000
33	1900	709000.0	56000.0	76094000	9.3	0.7
34	1901	742000.0	61000.0	77584000	9.6	0.8
35	1902	776000.0	61000.0	79163000	9.8	0.8
36	1903	818000.0	65000.0	80632000	10.1	0.8
37	1904	815000.0	66000.0	82166000	9.9	0.8
...	...	...	...	...	...	...
129	1996	NaN	NaN	269714000	NaN	NaN
130	1997	2384000.0	1163000.0	272958000	8.7	4.3
131	1998	2244000.0	1135000.0	276154000	8.1	4.1
132	1999	2358000.0	1145245.0	279328000	8.4	4.1
133	2000	2315000.0	944000.0	282398000	8.2	3.3

101 rows × 6 columns

fig= plt.figure(figsize=(10,12))
plt.bar(df2['Marriages_per_1000'],df2['Divorces_per_1000'], label="marriages and divorces per capita",color='brown')
plt.legend()
# The following commands add labels to our figure.
plt.xlabel('Marriages',fontsize=12,color='blue')
plt.ylabel('Divorces',fontsize=12,color='blue')
plt.title('Marriages and Divorces per capita in the U.S. between 1900, 1950 and 2000',fontsize=12,color='green')
plt.show()

QUESTION 8

Create a horizontal bar chart that compares the deadliest actors in Hollywood. Sort the actors by their kill count and label each bar with the corresponding actor's name. Don't forget to label your axes!

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_csv("actor_kill_counts.csv")
fig= plt.figure(figsize=(10,8))
plt.barh(df['Actor'], df['Count'],color='rgb')
plt.ylabel("Actor",fontsize=12,color='red')
plt.xlabel("Kill count",fontsize=12,color='red')
plt.title("Actor's kill count",fontsize=15,color='blue')
plt.show()

<ipython-input-61-7c5d8ed786f1>:6: MatplotlibDeprecationWarning: Using a string of single character colors as a color sequence is deprecated since 3.2 and will be removed two minor releases later. Use an explicit list instead.
  plt.barh(df['Actor'], df['Count'],color='rgb')

QUESTION 9

Create a pie chart showing the fraction of all Roman Emperors that were assassinated.

Make sure that the pie chart is an even circle, labels the categories, and shows the percentage breakdown of the categories.

from plotly.offline import init_notebook_mode, iplot, plot
import plotly as py
roman = pd.read_csv('roman-emperor-reigns.csv')
roman.Cause_of_Death.unique()

array(['Possibly assassinated', 'Assassinated', 'Suicide',
       'Natural causes', 'Illness', 'Executed', 'Killed in battle',
       'Died in captivity'], dtype=object)

assassinated = roman.Cause_of_Death.value_counts()
fig = {
  "data": [
    {
      "values": list(assassinated.values),
      "labels": list(assassinated.index),
      "domain": {"x": [.4, .4]},
      "name": "Cause of death",
      "hoverinfo":"label+percent",
      "hole": 0,
      "type": "pie"
    },],
  "layout": {
        "title":"Cause of death of Roman Emperors",
        "annotations": [
            { "font": { "size": 20},
              "showarrow": False,
              "text": "",
                "x": 0.20,
                "y": 1                     
            },
        ]
    }
}
iplot(fig)

QUESTION 10

Create a scatter plot showing the relationship between the total revenue earned by arcades and the number of Computer Science PhDs awarded in the U.S. between 2000 and 2009.

Don't forget to label your axes! Color each dot according to its year.

df1=pd.read_csv(r'arcade-revenue-vs-cs-doctorates.csv')
fig= plt.figure(figsize=(12,9))
plt.scatter(x="Total Arcade Revenue (billions)",y="Computer Science Doctorates Awarded (US)",c=df1.Year,cmap="viridis",data=df1,s=200)
plt.xlabel('Total Arcade Revenue (billions)',fontsize=10,color='blue')
plt.ylabel('Computer Science Doctorates Awarded (US)',fontsize=10,color='blue')
plt.show()